Location-based services (LBS) which bring so much convenience to our daily life have been intensively studied over the years. If an LBS provider has a malicious intention to breach the user privacy by tracking the users' routes to their destinations, it incurs a serious threat. Most existing techniques have addressed privacy protection mainly for snapshot queries. However, providing privacy protection for continuous queries is of importance, since a malicious LBS can easily obtain user privacy information by observing a sequence of successive query requests. In this paper, we propose a comprehensive trajectory privacy technique and combine ambient conditions to cloak location information. We first propose a r-anonymity mechanism which pre-processes a set of similar trajectories R to blur the actual trajectory of a user. We introduce a novel time-obfuscated technique which breaks the sequence of the query issuing time for a user to confuse the LBS so it does not know the user trajectory by sending a query randomly from a set of locations residing at the different trajectories in R. Despite the randomness incurred from the obfuscation process, the experimental results showed that our trajectory privacy technique maintains the correctness of the query results at a competitive computational cost.